Machine learning engineer jobs in 2026: Apple leads with 37 open roles
Apple has 37 open machine learning engineer roles in HireIndex’s August 31, 2026 snapshot — more than OpenAI (7), Databricks (8), and Scale AI (11) combined. That comparison isn’t quite like-for-like: the index sees each company only through the job feeds it posts to, and Apple’s roles come almost entirely from TheMuse while OpenAI’s come from its Ashby board alone. Apple still tops the category across the 799 companies tracked, drawn from Ashby, Greenhouse, Lever, LinkedIn, Adzuna, and TheMuse.
Machine Learning Engineer is the single largest job category in the index. At 460 open roles, it accounts for 22% of the 2,073 tracked AI/ML jobs — more than Data Scientist (264), Applied AI (159), and LLM Engineer (50) combined.
Which companies are hiring the most machine learning engineers?
| Company | ML Engineer Roles | Total AI Roles | Signal |
|---|---|---|---|
| Apple | 37 | 68 | Accelerating |
| 22 | 39 | Accelerating | |
| Spotify | 12 | 20 | Early-stage |
| Scale AI | 11 | 56 | Accelerating |
| Databricks | 8 | 70 | Accelerating |
| Capital One | 7 | 53 | Accelerating |
| OpenAI | 7 | 101 | Accelerating |
| JPMorganChase | 5 | — | — |
| Deepgram | 5 | 15 | Accelerating |
| Perplexity | 4 | 16 | Accelerating |
At Reddit, ML engineer roles make up 56% of total AI hiring. At Apple, the share is 54%. These companies aren’t spreading demand across research scientists, AI product managers, and strategy roles — they’re concentrating it on engineers who can ship models into production.
OpenAI and Databricks look different. OpenAI has 101 total AI roles but only 7 ML engineer titles — 7%. Databricks has 70 total roles but 8 ML engineers. Both companies spread demand across a wider range of specialties.
Where are machine learning engineer roles located?
274 of the 460 ML engineer roles — 59% — list as fully remote. Among cities with a physical presence, the distribution is uneven:
| City | ML Engineer Roles |
|---|---|
| London | 85 |
| San Francisco | 29 |
| New York | 23 |
| Toronto | 14 |
| Seattle | 10 |
| Sydney | 7 |
| Austin | 6 |
| Berlin | 5 |
| Boston | 3 |
London’s 85 roles make it the largest in-person ML engineer market by a wide margin — nearly three times San Francisco’s 29. That reflects both the size of London’s tech sector and the role Hackajob plays as a staffing aggregator funneling postings from UK employers into the index. JPMorganChase, Spotify, Wayve, and ASOS each post London-based ML engineer roles as well.
Is machine learning engineer a senior-only role?
| Seniority | Count | Share |
|---|---|---|
| Mid | 235 | 51% |
| Senior | 137 | 30% |
| Senior-heavy (Staff/Principal) | 70 | 15% |
| Leadership | 9 | 2% |
| Junior | 9 | 2% |
Nine of 460 roles are labeled junior. That’s 2% — the same number as leadership roles. Candidates looking for a first ML engineering job through public postings are working with very thin supply: nine positions across 799 companies.
Mid-level is the dominant band at 51%. Companies are looking for engineers who own a project end-to-end, not researchers still finding their footing. Senior and staff roles account for another 45% between them.
What is Apple building with 37 ML engineer hires?
Apple’s roles span at least seven distinct product areas: Siri speech models, Ads predictions, News and Books recommendations, Health AI, AppleCare demand forecasting, iCloud, and an ML Compute infrastructure layer Apple calls its “Private Cloud Compute” platform. Tech signals in the index for Apple include LLM Training, Agentic AI, Agentic Systems, Computer Vision, Conversational Speech/NLP, GenAI, and Private Cloud Computing.
Apple’s weekly count in the index reads 4 (Aug 18), 0 (Aug 24), 58 (Aug 25), 68 (Aug 31), and that curve is about our coverage rather than Apple’s hiring. 62 of the 68 roles arrive through TheMuse, a feed that was returning almost nothing here until a scraper fix on August 25. The zero a day earlier is a separate gap: Adzuna was Apple’s only source that week and failed 10 of its queries with HTTP 503. Apple’s postings run across Cupertino, Santa Clara, Sunnyvale, Austin, San Diego, and Seattle.
Most Cupertino roles carry a “Sr.” or “Senior” prefix and sit within the AIML platform division. Austin and San Diego roles lean toward applied ML for specific product lines — quality systems, demand planning, and similar production workloads.
Apple’s hiring signal is classified as “accelerating” with a score of 10, the index maximum — but that signal is computed from the same four-week curve, so for Apple it’s reading coverage as much as hiring. There’s no trustworthy week-over-week number until the September 7 snapshot. What the composition does support is the analyst note in the index, which describes Apple’s build-out as “a mature, well-funded build-out rather than exploratory hiring.”
What does the role mix say about where ML engineering is going?
Three things stand out across the full 460-role dataset.
First, Apple’s dominance suggests that the consumer device ecosystem is now competing directly with AI labs for ML talent — and winning on volume. Whether it competes on prestige is a separate question, but it posts more roles than any lab in the index.
Second, the remote share (59%) is high relative to AI roles overall. ML engineering pipelines are mature enough that companies are comfortable hiring without co-location. That is less true for frontier research and increasingly true for production ML.
Third, the 2% junior rate is not going to change meaningfully in the near term. None of the top-10 ML engineer hirers has a significant junior cohort. The pipeline into mid-level ML roles runs through graduate programs, bootcamp-adjacent paths, and adjacent titles (data scientist, software engineer), not through an explicit junior tier at companies like Apple or Reddit.